OBJECTIVES: Big data analysis is receiving increasing attention in many industries, including healthcare. Visualization plays an important role not only in intuitively showing the results of data analysis but also in the whole process of collecting, cleaning, analyzing, and sharing data. This paper presents a procedure for the interactive visualization and analysis of healthcare data using Tableau as a business intelligence tool. METHODS: Starting with installation of the Tableau Desktop Personal version 10.3, this paper describes the process of understanding and visualizing healthcare data using an example. The example data of colon cancer patients were obtained from health insurance claims in years 2012 and 2013, provided by the Health Insurance Review and Assessment Service. RESULTS: To explore the visualization of healthcare data using Tableau for beginners, this paper describes the creation of a simple view for the average length of stay of colon cancer patients. Since Tableau provides various visualizations and customizations, the level of analysis can be increased with small multiples, view filtering, mark cards, and Tableau charts. CONCLUSIONS: Tableau is a software that can help users explore and understand their data by creating interactive visualizations. The software has the advantages that it can be used in conjunction with almost any database, and it is easy to use by dragging and dropping to create an interactive visualization expressing the desired format.
Artificial Intelligence ; Colonic Neoplasms ; Commerce ; Data Display ; Delivery of Health Care* ; Humans ; Information Storage and Retrieval ; Insurance, Health ; Intelligence ; Length of Stay ; Statistics as Topic

OBJECTIVES: For earlier detection of infectious disease outbreaks, a digital syndromic surveillance system based on search queries or social media should be utilized. By using real-time data sources, a digital syndromic surveillance system can overcome the limitation of time-delay in traditional surveillance systems. Here, we introduce an approach to develop such a digital surveillance system. METHODS: We first explain how the statistics data of infectious diseases, such as influenza and Middle East Respiratory Syndrome (MERS) in Korea, can be collected for reference data. Then we also explain how search engine queries can be retrieved from Google Trends. Finally, we describe the implementation of the prediction model using lagged correlation, which can be calculated by the statistical packages, i.e., SPSS (Statistical Package for the Social Sciences). RESULTS: Lag correlation analyses demonstrated that search engine data/Twitter have a significant temporal relationship with influenza and MERS data. Therefore, the proposed digital surveillance system can be used to predict infectious disease outbreaks earlier. CONCLUSIONS: This prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase.
Communicable Diseases* ; Coronavirus Infections ; Disease Outbreaks* ; Influenza, Human ; Information Storage and Retrieval ; Internet ; Korea ; Methods* ; Search Engine ; Social Media*

OBJECTIVES: Families of ethnic minority persons with dementia often seek help at later stages of the disease. Little is known about the effectiveness of various methods in supporting ethnic minority dementia patients' caregivers. The objective of the study was to identify smartphone and computer usage among family caregivers of dementia patients (i.e., Korean and Vietnamese Americans) to develop dementia-care education programs for them. METHODS: Participants were asked various questions related to their computer or smartphone usage in conjunction with needs-assessment interviews. Flyers were distributed at two ethnic minority community centers in Southern California. Snowball recruitment was also utilized to reach out to the families of dementia patients dwelling in the community. RESULTS: Thirty-five family caregivers, including 20 Vietnamese and 15 Korean individuals, participated in this survey. Thirty participants (30 of 35, 85.7%) were computer users. Among those, 76.7% (23 of 30) reported daily usage and 53% (16 of 30) claimed to use social media. A majority of the participants (31 of 35, 88.6%) reported that they owned smartphones. More than half of smartphone users (18 of 29, 62%) claimed to use social media applications. Many participants claimed that they could not attend in-class education due to caregiving and/or transportation issues. CONCLUSIONS: Most family caregivers of dementia patients use smartphones more often than computers, and more than half of those caregivers communicate with others through social media apps. A smartphone-app-based caregiver intervention may serve as a more effective approach compared to the conventional in-class method. Multiple modalities for the development of caregiver interventions should be considered.
Asian Americans* ; Asian Continental Ancestry Group* ; California ; Caregivers* ; Dementia* ; Education* ; Humans ; Methods ; Minority Groups ; Smartphone* ; Social Media ; Transportation

OBJECTIVES: Biosignal data include important physiological information. For that reason, many devices and systems have been developed, but there has not been enough consideration of how to collect and integrate raw data from multiple systems. To overcome this limitation, we have developed a system for collecting and integrating biosignal data from two patient monitoring systems. METHODS: We developed an interface to extract biosignal data from Nihon Kohden and Philips monitoring systems. The Nihon Kohden system has a central server for the temporary storage of raw waveform data, which can be requested using the HL7 protocol. However, the Philips system used in our hospital cannot save raw waveform data. Therefore, our system was connected to monitoring devices using the RS232 protocol. After collection, the data were transformed and stored in a unified format. RESULTS: From September 2016 to August 2017, we collected approximately 117 patient-years of waveform data from 1,268 patients in 79 beds of five intensive care units. Because the two systems use the same data storage format, the application software could be run without compatibility issues. CONCLUSIONS: Our system collects biosignal data from different systems in a unified format. The data collected by the system can be used to develop algorithms or applications without the need to consider the source of the data.
Electrocardiography ; Humans ; Information Storage and Retrieval ; Intensive Care Units ; Monitoring, Physiologic* ; Photoplethysmography

OBJECTIVES: The purpose of this study was to develop and cross-culturally validate the Korean version of SMArtphone's uSability Heuristics (K-SMASH). METHODS: In the study, it was used the adaptation process consisted of five stages, namely, translation, synthesis, back translation, expert committee review, and pretesting. In the pretesting stage, a mobile application, using the prefinal K-SMASH, was evaluated for the severity of usability problems by three experts in computer science and informatics. Each participant completed the evaluation and was interviewed about their understanding, interpretation, and opinion of the cultural relevance of the prefinal K-SMASH. Next, we reviewed the differences in the experts’ opinions and the questionnaire results. RESULTS: Twelve SMASH items, words and sentences, were translated, back translated, and revised, considering the conceptual meaning in the context of the Korean culture, by experts in various fields, including a Korean linguist and a bilingual translator, through the first stage to the fourth stage. In the pretesting stage, the results showed no major differences among the severity ratings of participants. Furthermore, all participants answered that there were no critical discrepancies or inconsistencies with the cultural relevance of the prefinal K-SMASH. CONCLUSIONS: The results of the study provide preliminary evidence that the modified K-SMASH can be used for heuristic evaluation, one of the usability tests, when developing applications in Korea.
Buprenorphine ; Evaluation Studies as Topic ; Heuristics* ; Informatics ; Korea ; Mobile Applications ; Smartphone

OBJECTIVES: Health information technology (IT) can assist healthcare providers in ordering medication and adhering to guidelines while improving communication among providers and the quality of care. However, the relationship between health IT and Case Mix Index (CMI) has not been thoroughly investigated; therefore, this study aimed to clarify this relationship. METHODS: To examine the effect of health IT on CMI, a generalized estimation equation (GEE) was applied to two years of California hospital data. RESULTS: We found that IT was positively associated with CMI, indicating that increased IT adoption could lead to a higher CMI or billing though DRG up-coding. This implies that hospitals' revenue could increase around $40,000 by increasing IT investment by 10%. CONCLUSIONS: The positive association between IT and CMI implies that IT adoption itself could lead to higher patient billings. Generally, a higher CMI in a hospital indicates that the hospital provides expensive services with higher coding and therefore receives more money from patients. Therefore, measures to prevent upcoding through IT systems should be implemented.
California ; Clinical Coding ; Diagnosis-Related Groups* ; Health Personnel ; Humans ; Investments ; Medical Informatics*

OBJECTIVES: This study aimed to identify problems and issues that arise with the implementation of online health information exchange (HIE) systems in a medical environment and to identify solutions to facilitate the successful operation of future HIE systems in primary care clinics and hospitals. METHODS: In this study, the issues that arose during the establishment and operation of an HIE system in a hospital were identified so that they could be addressed to enable the successful establishment and operation of a standard-based HIE system. After the issues were identified, they were reviewed and categorized by a group of experts that included medical information system experts, doctors, medical information standard experts, and HIE researchers. Then, solutions for the identified problems were derived based on the system development, operation, and improvement carried out during this work. RESULTS: Twenty-one issues were identified during the implementation and operation of an online HIE system. These issues were then divided into four categories: system architecture and standards, documents and data items, consent of HIE, and usability. We offer technical and policy recommendations for various stakeholders based on the experiences of operating and improving the online HIE system in the medical field. CONCLUSIONS: The issues and solutions identified in this study regarding the implementation and operate of an online HIE system can provide valuable insight for planners to enable them to successfully design and operate such systems at a national level in the future. In addition, policy support from governments is needed.
Electronic Health Records ; Health Information Exchange* ; Health Information Management ; Health Level Seven ; Information Systems ; Primary Health Care

OBJECTIVES: To overcome challenges in the implementation of electronic dental record systems in a low-resource setting, it is crucial to know the level of users’ satisfaction with the existing system of paper-based dental records and their perceptions of electronic dental records. METHODS: A cross-sectional paper-based questionnaire survey was conducted among Myanmar dental professionals who worked in one of two teaching hospitals or in private dental clinics. Descriptive data were analyzed and regression analysis was carried out to identify factors influencing perceptions of electronic dental records. RESULTS: Most dental professionals (>60%) were satisfied with just three out of six aspects of paper-based dental records (familiarity, flexibility, and portability). In addition, generalized positive perceptions were found among decision makers towards electronic dental records, and 86% of dentists indicated that they were willing to use them. Financial concerns were identified as the most important barrier to the implementation of electronic dental records among dentists who were not willing to use the proposed system. CONCLUSIONS: The first step towards implementing electronic dental records in Myanmar should be improvement of the content and structure of paper-based dental records, especially in private dental clinics. Utilization of appropriate open-source electronic dental record software in private dental clinics is recommended to address perceived issues around financial barriers. For the long term, we recommend providing further education and training in health informatics to healthcare professionals to facilitate the efficient use of electronic dental record software in Myanmar in the future.
Delivery of Health Care ; Dental Clinics ; Dental Records* ; Dentists ; Education ; Hospitals, Teaching ; Humans ; Informatics ; Myanmar* ; Pliability

OBJECTIVES: This study examined the validity of the principal diagnoses on discharge summaries and coding assessments. METHODS: Data were collected from the National Health Security Office (NHSO) of Thailand in 2015. In total, 118,971 medical records were audited. The sample was drawn from government hospitals and private hospitals covered by the Universal Coverage Scheme in Thailand. Hospitals and cases were selected using NHSO criteria. The validity of the principal diagnoses listed in the “Summary and Coding Assessment” forms was established by comparing data from the discharge summaries with data obtained from medical record reviews, and additionally, by comparing data from the coding assessments with data in the computerized ICD (the data base used for reimbursement-purposes). RESULTS: The summary assessments had low sensitivities (7.3%–37.9%), high specificities (97.2%–99.8%), low positive predictive values (9.2%–60.7%), and high negative predictive values (95.9%–99.3%). The coding assessments had low sensitivities (31.1%–69.4%), high specificities (99.0%–99.9%), moderate positive predictive values (43.8%–89.0%), and high negative predictive values (97.3%–99.5%). The discharge summaries and codings often contained mistakes, particularly the categories “Endocrine, nutritional, and metabolic diseases”, “Symptoms, signs, and abnormal clinical and laboratory findings not elsewhere classified”, “Factors influencing health status and contact with health services”, and “Injury, poisoning, and certain other consequences of external causes”. CONCLUSIONS: The validity of the principal diagnoses on the summary and coding assessment forms was found to be low. The training of physicians and coders must be strengthened to improve the validity of discharge summaries and codings.
Clinical Coding* ; Diagnosis* ; Hospitals, Private ; International Classification of Diseases* ; Medical Records ; Poisoning ; Sensitivity and Specificity ; Thailand* ; Universal Coverage

OBJECTIVES: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. METHODS: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and we evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). RESULTS: We designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. The experimental results showed that the proposed model achieved an accuracy and a specificity of 66.23% and 75.32%, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. CONCLUSIONS: The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.
Classification* ; Dataset ; Delivery of Health Care ; Forests ; Korea ; Learning ; Machine Learning ; Methods ; Nutrition Surveys ; Quality of Life ; Sensitivity and Specificity ; Support Vector Machine